boot_ci {cutpointr} | R Documentation |
Given a cutpointr
object that includes bootstrap results
this function calculates a bootstrap
confidence interval for a selected variable.
Missing values are removed before calculating the quantiles. In the case
of multiple optimal cutpoints all cutpoints / metric values are included
in the calculation.
Values of the selected variable are returned for the percentiles alpha / 2
and 1 - alpha / 2. The metrics in the bootstrap data frames of
cutpointr
are suffixed with _b
and _oob
to indicate
in-bag and out-of-bag, respectively. For example, to calculate quantiles
of the in-bag AUC variable = AUC_b
should be set.
boot_ci(x, variable, in_bag = TRUE, alpha = 0.05)
x |
(character) The numeric independent (predictor) variable. |
variable |
Variable to calculate CI for |
in_bag |
Whether the in-bag or out-of-bag results should be used for testing |
alpha |
Alpha level. Quantiles of the bootstrapped values are returned for (alpha / 2) and 1 - (alpha / 2). |
A data frame with the columns quantile and value
Other main cutpointr functions:
add_metric()
,
boot_test()
,
cutpointr()
,
multi_cutpointr()
,
predict.cutpointr()
,
roc()
## Not run: opt_cut <- cutpointr(suicide, dsi, suicide, gender, metric = youden, boot_runs = 1000) boot_ci(opt_cut, optimal_cutpoint, in_bag = FALSE, alpha = 0.05) boot_ci(opt_cut, acc, in_bag = FALSE, alpha = 0.05) boot_ci(opt_cut, cohens_kappa, in_bag = FALSE, alpha = 0.05) boot_ci(opt_cut, AUC, in_bag = TRUE, alpha = 0.05) ## End(Not run)